A Sentinel-2 Based Multi-Temporal Monitoring Framework for Wind and Bark Beetle Detection and Damage Mapping
Abstract
1. Introduction
2. Materials and Methods
2.1. Study Area
The Vaia Storm
2.2. Data
2.3. Methods
2.3.1. Single Bands and Vegetation Indices
2.3.2. Supervised Classification of Multi-Temporal Imagery
2.3.3. Post-Classification Forest-Cover Change Detection
3. Results
3.1. Single Bands and Vegetation Indices Reflectance Values
3.2. Supervised Classification
3.2.1. Spectral Signatures
3.2.2. Maps of Damage
3.3. Post-Classification Change Detection
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
06/2017 RF | ||||||
Confusion Matrix and Statistics | ||||||
Reference | ||||||
Prediction | healthy | red_attack | shadows | stressed | ||
healthy | 24 | 1 | 0 | 0 | ||
red_attack | 0 | 31 | 0 | 3 | ||
shadows | 0 | 0 | 26 | 0 | ||
stressed | 1 | 1 | 0 | 32 | ||
Overall Statistics | ||||||
Accuracy | 0.9535 | |||||
95% CI | (0.9015, 0.9827) | |||||
No-Information Rate | 0.2713 | |||||
p-Value (Acc > NIR) | <2.2 × 10−16 | |||||
Kappa | 0.9377 | |||||
06/2017 ANN | ||||||
Confusion Matrix and Statistics | ||||||
Reference | ||||||
Prediction | healthy | red_attack | shadows | stressed | ||
healthy | 33 | 1 | 0 | 1 | ||
red_attack | 0 | 31 | 0 | 6 | ||
shadows | 0 | 0 | 26 | 0 | ||
stressed | 2 | 1 | 0 | 28 | ||
Overall Statistics | ||||||
Accuracy | 0.9147 | |||||
95% CI | (0.8525, 0.9567) | |||||
No-Information Rate | 0.2713 | |||||
p-Value (Acc > NIR) | <2.2 × 10−16 | |||||
Kappa | 0.8859 | |||||
07/2018 RF | ||||||
Confusion Matrix and Statistics | ||||||
Reference | ||||||
Prediction | healthy | red_attack | shadows | stressed | ||
healthy | 23 | 1 | 0 | 0 | ||
red_attack | 0 | 24 | 0 | 1 | ||
shadows | 0 | 0 | 23 | 0 | ||
stressed | 4 | 2 | 0 | 26 | ||
Overall Statistics | ||||||
Accuracy | 0.9231 | |||||
95% CI | (0.854, 0.9662) | |||||
No-Information Rate | 0.2596 | |||||
p-Value (Acc > NIR) | <2.2 × 10−16 | |||||
Kappa | 0.8973 | |||||
07/2018 ANN | ||||||
Confusion Matrix and Statistics | ||||||
Reference | ||||||
Prediction | healthy | red_attack | shadows | stressed | ||
healthy | 26 | 0 | 0 | 3 | ||
red_attack | 0 | 26 | 1 | 0 | ||
shadows | 0 | 0 | 22 | 0 | ||
stressed | 1 | 1 | 0 | 24 | ||
Overall Statistics | ||||||
Accuracy | 0.9423 | |||||
95% CI | (0.8787, 0.9785) | |||||
No-Information Rate | 0.2596 | |||||
p-Value (Acc > NIR) | <2.2 × 10−16 | |||||
Kappa | 0.9229 | |||||
05/2019 RF | ||||||
Confusion Matrix and Statistics | ||||||
Reference | ||||||
Prediction | healthy | shadows | stressed | vaia | ||
healthy | 23 | 0 | 1 | 0 | ||
shadows | 0 | 19 | 0 | 0 | ||
stressed | 0 | 0 | 23 | 0 | ||
vaia | 0 | 0 | 0 | 19 | ||
Overall Statistics | ||||||
Accuracy | 0.9882 | |||||
95% CI | (0.9362, 0.9997) | |||||
No-Information Rate | 0.2824 | |||||
p-Value (Acc > NIR) | <2.2 × 10−16 | |||||
Kappa | 0.9843 | |||||
05/2019 ANN | ||||||
Confusion Matrix and Statistics | ||||||
Reference | ||||||
Prediction | healthy | shadows | stressed | vaia | ||
healthy | 23 | 0 | 0 | 0 | ||
shadows | 0 | 19 | 0 | 0 | ||
stressed | 0 | 0 | 24 | 0 | ||
vaia | 0 | 0 | 0 | 19 | ||
Overall Statistics | ||||||
Accuracy | 1 | |||||
95% CI | (0.9575, 1) | |||||
No-Information Rate | 0.2824 | |||||
p-Value (Acc > NIR) | <2.2 × 10−16 | |||||
Kappa | 1 | |||||
07/2019 RF | ||||||
Confusion Matrix and Statistics | ||||||
Reference | ||||||
Prediction | healthy | red_attack | shadows | stressed | vaia | |
healthy | 45 | 0 | 0 | 0 | 0 | |
red_attack | 0 | 23 | 0 | 2 | 5 | |
shadows | 0 | 0 | 10 | 0 | 0 | |
stressed | 0 | 3 | 0 | 59 | 0 | |
vaia | 0 | 2 | 0 | 0 | 8 | |
Overall Statistics | ||||||
Accuracy | 0.9236 | |||||
95% CI | (0.8703, 0.9588) | |||||
No-Information Rate | 0.3885 | |||||
p-Value (Acc > NIR) | <2.2 × 10−16 | |||||
Kappa | 0.894 | |||||
07/2019 ANN | ||||||
Confusion Matrix and Statistics | ||||||
Reference | ||||||
Prediction | healthy | red_attack | shadows | stressed | vaia | |
healthy | 27 | 1 | 0 | 1 | 0 | |
red_attack | 0 | 21 | 0 | 1 | 2 | |
shadows | 0 | 0 | 11 | 0 | 0 | |
stressed | 2 | 3 | 0 | 32 | 1 | |
vaia | 0 | 3 | 0 | 0 | 13 | |
Overall Statistics | ||||||
Accuracy | 0.8814 | |||||
95% CI | (0.809, 0.9366) | |||||
No-Information Rate | 0.2881 | |||||
p-Value (Acc > NIR) | <2.2 × 10−16 | |||||
Kappa | 0.8462 | |||||
09/2019 RF | ||||||
Confusion Matrix and Statistics | ||||||
Reference | ||||||
Prediction | healthy | red_attack | shadows | stressed | vaia | |
healthy | 25 | 0 | 0 | 1 | 0 | |
red_attack | 1 | 17 | 0 | 2 | 1 | |
shadows | 0 | 0 | 3 | 0 | 0 | |
stressed | 1 | 0 | 0 | 24 | 0 | |
vaia | 0 | 1 | 0 | 0 | 20 | |
Overall Statistics | ||||||
Accuracy | 0.9271 | |||||
95% CI | (0.8555, 0.9702) | |||||
No-Information Rate | 0.2812 | |||||
p-Value (Acc > NIR) | <2.2 × 10−16 | |||||
Kappa | 0.9042 | |||||
09/2019 ANN | ||||||
Confusion Matrix and Statistics | ||||||
Reference | ||||||
Prediction | healthy | red_attack | shadows | stressed | vaia | |
healthy | 24 | 4 | 0 | 2 | 0 | |
red_attack | 2 | 12 | 0 | 3 | 0 | |
shadows | 0 | 0 | 3 | 0 | 0 | |
stressed | 1 | 1 | 0 | 21 | 0 | |
vaia | 0 | 1 | 0 | 1 | 21 | |
Overall Statistics | ||||||
Accuracy | 0.8438 | |||||
95% CI | (0.7554, 0.9098) | |||||
No-Information Rate | 0.2812 | |||||
p-Value (Acc > NIR) | <2.2 × 10−16 | |||||
Kappa | 0.7939 | |||||
07/2020 RF | ||||||
Confusion Matrix and Statistics | ||||||
Reference | ||||||
Prediction | healthy | red_attack | shadows | stressed | vaia | |
healthy | 31 | 0 | 0 | 3 | 0 | |
red_attack | 0 | 28 | 0 | 2 | 0 | |
shadows | 0 | 0 | 43 | 2 | 0 | |
stressed | 4 | 7 | 0 | 28 | 0 | |
vaia | 0 | 0 | 0 | 0 | 7 | |
Overall Statistics | ||||||
Accuracy | 0.8839 | |||||
95% CI | (0.8227, 0.9297) | |||||
No-Information Rate | 0.2774 | |||||
p-Value (Acc > NIR) | <2.2 × 10−16 | |||||
Kappa | 0.8487 | |||||
07/2020 ANN | ||||||
Confusion Matrix and Statistics | ||||||
Reference | ||||||
Prediction | healthy | red_attack | shadows | stressed | vaia | |
healthy | 27 | 0 | 0 | 4 | 0 | |
red_attack | 1 | 28 | 0 | 6 | 0 | |
shadows | 0 | 0 | 43 | 6 | 0 | |
stressed | 7 | 7 | 0 | 19 | 0 | |
vaia | 0 | 0 | 0 | 0 | 7 | |
Overall Statistics | ||||||
Accuracy | 0.8 | |||||
95% CI | (0.7283, 0.8599) | |||||
No-Information Rate | 0.2774 | |||||
p-Value (Acc > NIR) | <2.2 × 10−16 | |||||
Kappa | 0.7389 |
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Number | Date | Data |
---|---|---|
1 | 20 June 2017 | Sentinel-2 L2A |
2 | 2 August 2017 | Sentinel-2 L2A |
3 | 29 August 2017 | Sentinel-2 L2A |
4 | 6 May 2018 | Sentinel-2 L2A |
5 | 30 July 2018 | Sentinel-2 L2A |
6 | 17 August 2018 | Sentinel-2 L2A |
7 | 28 September 2018 | Sentinel-2 L2A |
8 | 24 May 2019 | Sentinel-2 L2A |
9 | 30 June 2019 | Sentinel-2 L2A |
10 | 27 August 2019 | Sentinel-2 L2A |
11 | 21 September 2019 | Sentinel-2 L2A |
13 | 7 July 2020 | Sentinel-2 L2A |
14 | 29 July 2020 | Sentinel-2 L2A |
15 | 15 September 2020 | Sentinel-2 L2A |
Index | Sentinel-2 Bands | Application |
---|---|---|
NDWI | NIR, SWIR2 | Water content |
NDVI | NIR, Red | Greenness |
DWSI | NIR, Red | Greenness |
NMDI | NIR, Green, SWIR1, Red | Water content |
NDRS | Red, SWIR1 | Greenness, water content |
REIP | Red, RedEdge2, RedEdge1 | Greenness, water content |
NDREI1 | RedEdge2, RedEdge1 | Chlorophyll, biomass |
NDREI2 | RedEdge3, RedEdge1 | Chlorophyll, biomass |
RENDVI | Red, RedEdge1, RedEdge2 | Greenness, biomass |
TCW | Blue, Green, Red, NIR, SWIR1, SWIR2 | Water content |
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Candotti, A.; De Giglio, M.; Dubbini, M.; Tomelleri, E. A Sentinel-2 Based Multi-Temporal Monitoring Framework for Wind and Bark Beetle Detection and Damage Mapping. Remote Sens. 2022, 14, 6105. https://doi.org/10.3390/rs14236105
Candotti A, De Giglio M, Dubbini M, Tomelleri E. A Sentinel-2 Based Multi-Temporal Monitoring Framework for Wind and Bark Beetle Detection and Damage Mapping. Remote Sensing. 2022; 14(23):6105. https://doi.org/10.3390/rs14236105
Chicago/Turabian StyleCandotti, Anna, Michaela De Giglio, Marco Dubbini, and Enrico Tomelleri. 2022. "A Sentinel-2 Based Multi-Temporal Monitoring Framework for Wind and Bark Beetle Detection and Damage Mapping" Remote Sensing 14, no. 23: 6105. https://doi.org/10.3390/rs14236105
APA StyleCandotti, A., De Giglio, M., Dubbini, M., & Tomelleri, E. (2022). A Sentinel-2 Based Multi-Temporal Monitoring Framework for Wind and Bark Beetle Detection and Damage Mapping. Remote Sensing, 14(23), 6105. https://doi.org/10.3390/rs14236105